package net.bwie.realtime.dws.log.job;

import com.alibaba.fastjson.JSON;
import net.bwie.realtime.dws.log.bean.PageViewBean;
import net.bwie.realtime.dws.log.function.PageViewBeanMapFunction;
import net.bwie.realtime.dws.log.function.PageViewReportReduceFunction;
import net.bwie.realtime.dws.log.function.PageViewReportWindowFunction;
import net.bwie.realtime.jtp.utils.DorisUtil;
import net.bwie.realtime.jtp.utils.KafkaUtil;
import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;

import java.time.Duration;

/**
 * 当日APP流量日志数据进行实时汇总统计，按照分钟级别窗口汇总计算
 *      粒度：ar地区、ba品牌、ch渠道、is_new新老访客，
 *      指标：PV（页面浏览数）、浏览总时长、UV（唯一访客数）、SV（会话数）
 */
public class JtpTrafficPageViewMinuteWindowDwsJob {

    public static void main(String[] args) throws Exception{

        //执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        env.enableCheckpointing(300L);

        //读取数据源
        DataStream<String> pageStream = KafkaUtil.consumerKafka(env, "dwd-traffic-page-log");

        //数据转换
        DataStream<String> resultStream = handle(pageStream);
        resultStream.print("result");

        //数据输出
        DorisUtil.saveToDoris(
                resultStream,"jtp_realtime_report","dws_traffic_page_view_window_report"
        );

        //触发执行
        env.execute("JtpTrafficPageViewMinuteWindowDwsJob");
    }

    /**
     * 对页面浏览日志数据进行汇总计算
     * @param stream
     * @return
     */
    private static DataStream<String> handle(DataStream<String> stream) {
        //s1.按照设备id进行分组，用于计算uv,使用状态state记录今日是否第一次访问
        KeyedStream<String, String> midStream = stream.keyBy(
                json -> JSON.parseObject(json).getJSONObject("common").getString("mid")
        );
        //s2.将流中每条数据封装成实体类Bean对象
        DataStream<PageViewBean> beanStream = midStream.map(new PageViewBeanMapFunction());
        //s3.事件时间字段和水位线
        DataStream<PageViewBean> timeStream = beanStream.assignTimestampsAndWatermarks(
                WatermarkStrategy.<PageViewBean>forBoundedOutOfOrderness(Duration.ofSeconds(0))
                        .withTimestampAssigner(new SerializableTimestampAssigner<PageViewBean>() {
                            @Override
                            public long extractTimestamp(PageViewBean element, long recordTimestamp) {
                                return element.getTs();
                            }
                        })
        );
        //s4.分组keyBy:ar地区，ba品牌，ch渠道，is_new新老访客
        KeyedStream<PageViewBean, String> KeyedStream = timeStream.keyBy(
                bean -> bean.getBrand() + "," + bean.getChannel() + "," + bean.getProvince() + "," + bean.getIsNew()
        );
        //s5.开窗 滚动窗口,窗口大小设置为1分钟
        WindowedStream<PageViewBean, String, TimeWindow> windowStream = KeyedStream.window(
                TumblingEventTimeWindows.of(Time.minutes(1))
        );
        //s6.聚合，对窗口函数进行计算
        DataStream<String> reportStream = windowStream.reduce(
                new PageViewReportReduceFunction(),new PageViewReportWindowFunction()
        );

        return reportStream;
    }
}
